Magnetic resonance imaging and prediction of outcome in patients with major depressive disorder
Bibliographic record
Abstract
Whether magnetic resonance imaging studies can provide useful information to clinicians who treat people with major depressive disorder remains to be established. There are, however, several recent findings that suggest that likelihood of response may be predicted by imaging findings. For example, morphometric studies have examined whether hippocampus volume is associated with clinically meaningful outcomes such as response to treatment. In general, patients who remit have larger pretreatment hippocampus volumes bilaterally compared with those who do not remit. There are similar preliminary findings for the anterior cingulate cortex. There are also a number of functional imaging studies that have identified different activity patterns in those who are likely to respond to treatment compared with those who are not. Using positron emission tomography, investigators have reported different patterns of response to treatment in those treated with medication compared with those treated with psychotherapy. Some of the potential barriers to the routine use of imaging in psychiatric practice are reviewed briefly.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Review of MRI findings predicting treatment outcome in depression; clinical neuroscience.
It reviews clinical applications of neuroimaging for depression rather than studying evidence synthesis or research practice.
Clinical review of MRI predictors of treatment outcome in major depression; medical imaging, not metaresearch.
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".